Loading Signatures and Data
all_sigs <- readRDS(file.path(PATH,"data/signatures_symbol.rds"))
all_sigs <- all_sigs[lapply(all_sigs, length) != 0]
deseq_list <- readRDS(file.path(PATH,"data/deseq_list.rds"))
# PAMM Genelists
pamm_paths <- Sys.glob(file.path(PATH, "data/PAMM_Genelists/*"))
pamm_names <- lapply(pamm_paths, stringr::str_match, pattern="genelist-\\s*(.*?)\\s*.xlsx")
pamm_names <- unlist(lapply(pamm_names, function(x) x[2]))
pamm_genelists <- lapply(pamm_paths, openxlsx::read.xlsx, colNames = FALSE)
pamm_genelists <- lapply(pamm_genelists, function(x) x$X2)
names(pamm_genelists) <- pamm_names
hallmark_genesets <- msigdb_download(species="Mus musculus", category="H")
dat <- readRDS(file.path(PATH,"data/4T1_mets_sExp.rds"))
background <- unique(unlist(SummarizedExperiment::rowData(dat)$mgi_symbol))
#Other genesets
methyltransferase_genes <- read.delim(file.path(PATH, "data/methyltransferase_genes_ncbi.txt"))
dnmt_genes <- methyltransferase_genes$Symbol[methyltransferase_genes$Symbol %in% background]
hallmark_genesets$DNMT <- dnmt_genes
h3_meth_genes <- c("PRC1", "PRC2", "EZH2", "EED", "SUZ12", "RING1", "RNF2", "BRD2", "BRD3", "BRD4", "KDM1A", "KDM2A", "KDM3A", "KDM4A", "KDM5A")
library(babelgene)
mouse_h3_meth_genes <- babelgene::orthologs(genes = h3_meth_genes, species = "mouse")
hallmark_genesets$H3METH <- mouse_h3_meth_genes$symbol
hypeR enrichment
Hypergeometric Hallmark
hall_hyp <- hypeR::hypeR(all_sigs,genesets=hallmark_genesets,background=background, test="hypergeometric", fdr=0.05)
hypeR::hyp_dots(hall_hyp,merge=TRUE,top=15)

hypeR::rctbl_build(hall_hyp)
PAMM
pamm_hyp <- hypeR::hypeR(all_sigs,genesets=pamm_genelists,background=background,fdr=0.05)
hypeR::hyp_dots(pamm_hyp,merge=TRUE,top=25)

hypeR::rctbl_build(pamm_hyp)
if (do_save) {
hypeR::hyp_to_excel(hall_hyp,file.path(PATH,"data/hallmark_hyp.xlsx"))
}
KS-based enrichment
## Add gene symbols to DESeq tables
deseq_list <- lapply(deseq_list, function(Z) {
as.data.frame(Z) %>%
tibble::rownames_to_column(var="ensembl_id") %>%
dplyr::mutate(geneID=unlist(rowData(dat)[ensembl_id,"mgi_symbol"]))
})
## rank by up-regulation
rank_signatures_up <- lapply(
deseq_list, function(sig) sig %>%
dplyr::filter(geneID!="") %>%
dplyr::arrange(desc(log2FoldChange)) %>%
dplyr::select(geneID,log2FoldChange) %>%
tibble::deframe())
names(rank_signatures_up) <- paste(names(rank_signatures_up),"up",sep="_")
## rank by down-regulation
rank_signatures_dn <- lapply(
deseq_list,function(sig) sig %>%
dplyr::filter(geneID!="") %>%
dplyr::arrange(log2FoldChange) %>%
dplyr::select(geneID,log2FoldChange) %>%
tibble::deframe())
names(rank_signatures_dn) <- paste(names(rank_signatures_dn),"dn",sep="_")
rank_signatures <- c(rank_signatures_up,rank_signatures_dn)
rank_signatures <- rank_signatures[order(names(rank_signatures),decreasing = TRUE)]
Hallmarks + Pamm
all_genesets <- c(hallmark_genesets, pamm_genelists)
max_fdr <- 0.05
ks_hall <- hypeR::hypeR(signature=rank_signatures,genesets=all_genesets,test="ks",fdr=max_fdr,plotting=TRUE)
hyp_dots(ks_hall,merge=TRUE,fdr=max_fdr/5,top=25) + ggtitle(paste("FDR ≤", max_fdr/5))

hypeR::rctbl_build(ks_hall, show_hmaps=FALSE)
IS C UP
print(ks_hall$data$is_c_up$plots[ks_hall$data$is_c_up$data$fdr<=0.01])
## $HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION

##
## $HALLMARK_INTERFERON_GAMMA_RESPONSE

##
## $HALLMARK_KRAS_SIGNALING_DN

##
## $HALLMARK_XENOBIOTIC_METABOLISM

##
## $HALLMARK_TNFA_SIGNALING_VIA_NFKB

##
## $HALLMARK_BILE_ACID_METABOLISM

##
## $HALLMARK_INFLAMMATORY_RESPONSE

##
## $HALLMARK_OXIDATIVE_PHOSPHORYLATION

##
## $HALLMARK_MYOGENESIS

IS C Down
print(ks_hall$data$is_c_dn$plots[ks_hall$data$is_c_dn$data$fdr<=0.01])
## $HALLMARK_MYC_TARGETS_V1

##
## $HALLMARK_E2F_TARGETS

##
## $HALLMARK_G2M_CHECKPOINT

##
## $HALLMARK_CHOLESTEROL_HOMEOSTASIS

##
## $HALLMARK_MYC_TARGETS_V2

##
## $HALLMARK_MTORC1_SIGNALING

##
## $HALLMARK_MITOTIC_SPINDLE

##
## $HALLMARK_UNFOLDED_PROTEIN_RESPONSE

##
## $`Mouse Cell Cycle`

IR IS Up
print(ks_hall$data$ir_is_up$plots[ks_hall$data$ir_is_up$data$fdr<=0.01])
## $HALLMARK_G2M_CHECKPOINT

##
## $HALLMARK_E2F_TARGETS

##
## $HALLMARK_MYC_TARGETS_V1

##
## $HALLMARK_MITOTIC_SPINDLE

IR IS Down
print(ks_hall$data$ir_is_dn$plots[ks_hall$data$ir_is_dn$data$fdr<=0.01])
## $HALLMARK_MYOGENESIS

##
## $HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION

##
## $HALLMARK_OXIDATIVE_PHOSPHORYLATION

##
## $HALLMARK_INTERFERON_GAMMA_RESPONSE

##
## $HALLMARK_ADIPOGENESIS

IR C Up
print(ks_hall$data$ir_c_up$plots[ks_hall$data$ir_c_up$data$fdr<=0.01])
## $HALLMARK_XENOBIOTIC_METABOLISM

##
## $HALLMARK_TNFA_SIGNALING_VIA_NFKB

##
## $HALLMARK_INTERFERON_GAMMA_RESPONSE

IR C Down
print(ks_hall$data$ir_c_dn$plots[ks_hall$data$ir_c_dn$data$fdr<=0.01])
## $HALLMARK_MYC_TARGETS_V1

##
## $HALLMARK_MYC_TARGETS_V2

##
## $HALLMARK_MTORC1_SIGNALING

##
## $HALLMARK_CHOLESTEROL_HOMEOSTASIS

##
## $HALLMARK_E2F_TARGETS

##
## $HALLMARK_UNFOLDED_PROTEIN_RESPONSE

##
## $HALLMARK_G2M_CHECKPOINT

##
## $HALLMARK_MYOGENESIS

##
## $HALLMARK_TNFA_SIGNALING_VIA_NFKB
